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Symmetry
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Security, Communication and Privacy in Internet of Things: Symmetry and...
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Security, Communication and Privacy in Internet of Things: Symmetry and Advances — Volume II

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 4870

Special Issue Editor

Dr. Zichi Wang

E-Mail Website
Guest Editor
School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Interests: steganography; steganalysis; reversible data hiding; artificial intelligence security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning has been well developed in recent years. The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task. During the employment of neural network models, the problems of intellectual property, communication overhead, and privacy protection appear, including Multimedia and the Internet of Things. In the future, the above problems will be widespread in the Internet of Things. It is valuable to focus on the security, communication, and privacy in symmetry application. This Special Issue aims to highlight and advance contemporary research on the security, communication, and privacy in Internet of Things: Symmetry and Advances. We invite contributions of both original research and reviews of research that organize the recent research results in a unified and systematic way.

Dr. Zichi Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • information security
  • communication and privacy
  • multimedia processing
  • Internet of Things
  • symmetry application
  • intellectual property

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Published Papers (3 papers)

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Research

22 pages, 9020 KiB  
Article
A Low-Latency Noise-Aware Tone Mapping Operator for Hardware Implementation with a Locally Weighted Guided Filter
by Qianwang Liang, Tianyu Yan, Nan Wang, Zhiying Zhu and Jiongyao Ye
Symmetry 2024, 16(3), 356; https://doi.org/10.3390/sym16030356 - 15 Mar 2024
Viewed by 1398
Abstract
A tone mapping operator (TMO) is a module in the image signal processing pipeline that is used to convert high dynamic range images to low dynamic range images for display. Currently, state-of-the-art TMOs typically take complex algorithms and are implemented on graphics processing [...] Read more.
A tone mapping operator (TMO) is a module in the image signal processing pipeline that is used to convert high dynamic range images to low dynamic range images for display. Currently, state-of-the-art TMOs typically take complex algorithms and are implemented on graphics processing units, making it difficult to run with low latency on edge devices, and TMOs implemented in hardware circuits often lack additional noise suppression because of latency and hardware resource constraints. To address these issues, we proposed a low-latency noise-aware TMO for hardware implementation. Firstly, a locally weighted guided filter is proposed to decompose the luminance image into a base layer and a detail layer, with the weight function symmetric concerning the central pixel value of a window. Secondly, the mean and standard deviation of the basic layer and the detail layer are used to estimate the noise visibility according to the human visual characteristics. Finally, the gain for the detail layer is calculated to achieve adaptive noise suppression. In this process, luminance is first processed by the log2 function before being filtered and then symmetrically converted back to the linear domain by the exp2 function after compression. Meanwhile, the algorithms within the proposed TMO were optimized for hardware implementation to minimize latency and cache, achieving a low latency of 60.32 μs under video specification of 1080 P at 60 frames per second and objective metric smoothness in dark flat regions could be improved by more than 10% compared to similar methods. Full article
(This article belongs to the Special Issue Security, Communication and Privacy in Internet of Things: Symmetry and Advances — Volume II)
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11 pages, 1254 KiB  
Article
Diversified Cover Selection for Image Steganography
by Xinran Li, Daidou Guo and Chuan Qin
Symmetry 2023, 15(11), 2024; https://doi.org/10.3390/sym15112024 - 6 Nov 2023
Cited by 1 | Viewed by 1541
Abstract
This paper proposes a new cover selection method for steganography. We focus on the scenario that the available images for selection contain diversified sources, i.e., nature images and metaverse images. For the scenario, we design a targeted strategy to evaluate the suitability for [...] Read more.
This paper proposes a new cover selection method for steganography. We focus on the scenario that the available images for selection contain diversified sources, i.e., nature images and metaverse images. For the scenario, we design a targeted strategy to evaluate the suitability for steganography of a candidate image, which selects images according to the undetectability against steganalytic tools symmetrically. Firstly, steganalytic features of the candidate images are extracted. Then, the features are fed on a steganalytic classifier, and the possibility of carrying secret data is calculated for cover selection. As a result, the selected images are the best candidates to resist steganalysis. Experimental results show that our method performs better than existing cover selection schemes when checked by steganalytic tools. Full article
(This article belongs to the Special Issue Security, Communication and Privacy in Internet of Things: Symmetry and Advances — Volume II)
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17 pages, 599 KiB  
Article
Laplace-Domain Hybrid Distribution Model Based FDIA Attack Sample Generation in Smart Grids
by Yi Wu, Tong Zu, Naiwang Guo, Zheng Zhu and Fengyong Li
Symmetry 2023, 15(9), 1669; https://doi.org/10.3390/sym15091669 - 30 Aug 2023
Cited by 3 | Viewed by 1117
Abstract
False data injection attack (FDIA) is a deliberate modification of measurement data collected by the power grid using vulnerabilities in power grid state estimation, resulting in erroneous judgments made by the power grid control center. As a symmetrical defense scheme, FDIA detection usually [...] Read more.
False data injection attack (FDIA) is a deliberate modification of measurement data collected by the power grid using vulnerabilities in power grid state estimation, resulting in erroneous judgments made by the power grid control center. As a symmetrical defense scheme, FDIA detection usually uses machine learning methods to detect attack samples. However, existing detection models for FDIA typically require large-scale training samples, which are difficult to obtain in practical scenarios, making it difficult for detection models to achieve effective detection performance. In light of this, this paper proposes a novel FDIA sample generation method to construct large-scale attack samples by introducing a hybrid Laplacian model capable of accurately fitting the distribution of data changes. First, we analyze the large-scale power system sensing measurement data and establish the data distribution model of symmetric Laplace distribution. Furthermore, a hybrid Laplace-domain symmetric distribution model with multi-dimensional component parameters is constructed, which can induce a deliberate deviation in the state estimation from its safe value by injecting into the power system measurement. Due to the influence of the multivariate parameters of the hybrid Laplace-domain distribution model, the sample deviation generated by this model can not only obtain an efficient attack effect, but also effectively avoid the recognition of the FDIA detection model. Extensive experiments are carried out over IEEE 14-bus and IEEE 118-bus test systems. The corresponding results unequivocally demonstrate that our proposed attack method can quickly construct large-scale FDIA attack samples and exhibit significantly higher resistance to detection by state-of-the-art detection models, while also offering superior concealment capabilities compared to traditional FDIA approaches. Full article
(This article belongs to the Special Issue Security, Communication and Privacy in Internet of Things: Symmetry and Advances — Volume II)
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